The use of a cyber campus to support teaching and collaboration: An observation approach

The research reported in this paper is work in progress describing the experiences of the authors while using a cyber campus to support online learn- ing collaborative activities and investigate if a Transactive Memory System can be developed among group members, working together within a cyber campus in several pre-set tasks

A Modular Deep Learning Framework for Scene Understanding in Augmented Reality Applications

Taking as input natural images and videos augmented reality (AR) applications aim to enhance the real world with superimposed digital contents enabling interaction between the user and the environment. One important step in this process is automatic scene analysis and understanding that should be performed both in real time and with a good level of object recognition accuracy. In this work an end-to-end framework based on the combination of a Super Resolution network with a detection and recognition deep network has been proposed to increase performance and lower processing time. This novel approach has been evaluated on two different datasets: the popular COCO dataset whose real images are used for benchmarking many different computer vision tasks, and a generated dataset with synthetic images recreating a variety of environmental, lighting and acquisition conditions. The evaluation analysis is focused on small objects, which are more challenging to be correctly detected and recognised. The results show that the Average Precision is higher for smaller and low resolution objects for the proposed end-to-end approach in most of the selected conditions

Design methodology for 360° immersive video applications: the case study of a cultural heritage virtual tour

Three hundred sixty–degree (360°) immersive video applications for Head Mounted Display (HMD) devices offer great potential in providing engaging forms of experiential media solutions especially in Cultural Heritage education. Design challenges emerge though by this new kind of immersive media due to the 2D form of resources used for their construction, the lack of depth, the limited interaction and the need to address the sense of presence. In addition, the use of Virtual Reality (VR) headsets often causes nausea, or motion sickness effects imposing further implications in moderate motion design tasks. This paper introduces a methodological categorisation of tasks and techniques for the design of 360° immersive video applications. Following the design approach presented, a testbed application has been created as an immersive interactive virtual tour at the historical centre of the city of Rethymno in Crete, Greece, which has undergone user trials. Based on the analysis of the results of this study, a set of design guidelines for the implementation of 360° immersive video virtual tours is proposed

Intraclass Clustering-Based CNN Approach for Detection of Malignant Melanoma

This paper describes the process of developing a classification model for the effective detection of malignant melanoma, an aggressive type of cancer in skin lesions. Primary focus is given on fine-tuning and improving a state-of-the-art convolutional neural network (CNN) to obtain the optimal ROC-AUC score. The study investigates a variety of artificial intelligence (AI) clustering techniques to train the developed models on a combined dataset of images across data from the 2019 and 2020 IIM-ISIC Melanoma Classification Challenges. The models were evaluated using varying cross-fold validations, with the highest ROC-AUC reaching a score of 99.48%

Engaging immersive video consumers: Challenges regarding 360-degree gamified video applications

360-degree videos is a new medium that has gained the attention of the research community imposing challenges for creating more interactive and engaging immersive experiences. The purpose of this study is to introduce a set of technical and design challenges for interactive, gamified 360-degree mixed reality applications that immerse and engage users. The development of gamified applications refers to the merely incorporation of game elements in the interaction design process to attract and engage the user through playful interaction with the virtual world. The study presents experiments with the incorporation of series of game elements such as time pressure challenges, badges and user levels, storytelling narrative and immediate visual feedback to the interaction design logic of a mixed reality mobile gaming application that runs in an environment composed of 360-degree video and 3D computer generated objects. In the present study, the architecture and overall process for creating such an application is being presented along with a list of design implications and constraints. The paper concludes with future directions and conclusions on improving the level of immersion and engagement of 360-degree video consumers

Cognitive behaviour analysis based on facial information using depth sensors

Cognitive behaviour analysis is considered of high impor- tance with many innovative applications in a range of sectors including healthcare, education, robotics and entertainment. In healthcare, cogni- tive and emotional behaviour analysis helps to improve the quality of life of patients and their families. Amongst all the different approaches for cognitive behaviour analysis, significant work has been focused on emo- tion analysis through facial expressions using depth and EEG data. Our work introduces an emotion recognition approach using facial expres- sions based on depth data and landmarks. A novel dataset was created that triggers emotions from long or short term memories. This work uses novel features based on a non-linear dimensionality reduction, t-SNE, applied on facial landmarks and depth data. Its performance was eval- uated in a comparative study, proving that our approach outperforms other state-of-the-art features

An AI-Assisted Skincare Routine Recommendation System in XR

In recent years, there has been an increasing interest in the use of artificial intelligence (AI) and extended reality (XR) in the beauty industry. In this paper, we present an AI-assisted skin care recommendation system integrated into an XR platform. The system uses a convolutional neural network (CNN) to analyse an individual's skin type and recommend personalised skin care products in an immersive and interactive manner. Our methodology involves collecting data from individuals through a questionnaire and conducting skin analysis using a provided facial image in an immersive environment. This data is then used to train the CNN model, which recognises the skin type and existing issues and allows the recommendation engine to suggest personalised skin care products. We evaluate our system in terms of the accuracy of the CNN model, which achieves an average score of 93% in correctly classifying existing skin issues. Being integrated into an XR system, this approach has the potential to significantly enhance the beauty industry by providing immersive and engaging experiences to users, leading to more efficient and consistent skincare routines

AMNet: Memorability Estimation with Attention

In this paper we present the design and evaluation of an end-to-end trainable, deep neural network with a visual attention mechanism for memorability estimation in still images. We analyze the suitability of transfer learning of deep models from image classification to the memorability task. Further on we study the impact of the attention mechanism on the memorability estimation and evaluate our network on the SUN Memorability and the LaMem datasets. Our network outperforms the existing state of the art models on both datasets in terms of the Spearman's rank correlation as well as the mean squared error, closely matching human consistency